Abstract P346: Cohort Identification to Harness the Electronic Health Record for Cardiovascular Disease Prediction

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Sheila M Manemann ◽  
Jennifer St Sauver ◽  
Janet E Olson ◽  
Nicholas B Larson ◽  
Paul Y Takahashi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Health Record ◽  
Next Generation ◽  
Cvd Risk ◽  
Electronic Health

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

Abstract 36: The Role of Sleep as a Cardiovascular Health Metric: Does It Improve Cardiovascular Disease Risk Prediction? Results From The Multi-Ethnic Study of Atherosclerosis

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Nour Makarem ◽  
Cecilia Castro-Diehl ◽  
Marie-Pierre St-Onge ◽  
Susan Redline ◽  
Steven Shea ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Sleep Duration ◽  
Risk Prediction ◽  
Cardiovascular Health ◽  
Cvd Risk ◽  
Sleep Study ◽  
Lower Odds ◽  
Wrist Actigraphy ◽  
Incident Cases

Background: The AHA Life’s Simple 7 (LS7) is a measure of cardiovascular health (CVH). Sufficient and healthy sleep has been linked to higher LS7 scores and lower cardiovascular disease (CVD) risk, but sleep has not been included as a CVH metric. Hypothesis: A CVH score that includes the LS7 plus sleep metrics would be more strongly associated with CVD outcomes than the LS7 score. Methods: Participants were n=1920 diverse adults (mean age: 69.5 y) in the MESA Sleep Study who completed 7 days of wrist actigraphy, overnight in-home polysomnography, and sleep questionnaires. Logistic regression and Cox proportional hazards models were used to compare the LS7 score and 4 new CVH scores that incorporate aspects of sleep in relation to CVD prevalence and incidence (Table). There were 95 prevalent CVD events at the Sleep Exam and 93 incident cases during a mean follow up of 4.4y. Results: The mean LS7 score was 7.3, and the means of the alternate CVH scores ranged from 7.4 to 7.8. Overall, 63% of participants slept <7h, 10% had sleep efficiency <85%, 14% and 36% reported excess daytime sleepiness and insomnia, respectively, 47% had obstructive sleep apnea, and 39% and 25% had high night-to-night variability in sleep duration and sleep onset timing. The LS7 score was not significantly associated with CVD prevalence or incidence (Table). Those in the highest vs. lowest tertile of CVH score 1, that included sleep duration, and CVH score 2, that included sleep characteristics linked to CVD in the literature, had lower odds of prevalent CVD. Those in the highest vs. lowest tertile of CVH scores 3 and 4, which included sleep characteristics linked to cardiovascular risk in MESA, had lower odds of prevalent CVD and lower risk of developing CVD. Conclusions: CVH scores that include sleep were more strongly associated with CVD prevalence and incidence than the traditional LS7 score. The incorporation of sleep as a metric of CVH, akin to other health behaviors, may improve CVD risk prediction. Findings warrant confirmation in larger samples and over longer follow-up.

scholarly journals A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaona Jia ◽  
Mirza Mansoor Baig ◽  
Farhaan Mirza ◽  
Hamid GholamHosseini
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Clinical Laboratory ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

Hysterectomy utilization among newly diagnosed endometrial cancer patients by body mass index category: A real-world analysis using a claims and electronic health record database.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17112-e17112
Author(s):  
Debra E. Irwin ◽  
Ellen Thiel
Keyword(s):  
Endometrial Cancer ◽  
Electronic Health Record ◽  
Real World ◽  
Patient Population ◽  
Morbidly Obese ◽  
Health Record ◽  
Newly Diagnosed ◽  
Data Set ◽  
Electronic Health

e17112 Background: For endometrial cancer (EC), laparoscopic hysterectomy (LH) is an effective, minimally invasive surgical treatment; however, this approach may not be recommended for obese patients due to increased risk for complications. Methods: This retrospective study utilized insurance claims linked to electronic health record (EHR) data contained in the IBM MarketScan Explorys Claims-EHR Data Set. Newly diagnosed EC patients (1/1/2007 - 6/30/2017) with continuous enrollment during a 12-month baseline and 6-month follow-up period were selected. Patients were stratified into four BMI subgroups based on baseline BMI on the EHR: normal or underweight (BMI < 25), overweight (BMI 25- < 30), obese (BMI 30- < 40), morbidly obese (BMI > 40), and were required to have had a hysterectomy within the follow-up period. Emergency room visits and rehospitalization within 30 days of hysterectomy were measured. Results: A total of 1,090 newly-diagnosed EC patients met the selection criteria, of whom, 16% were normal/underweight, 19% were overweight, 39% were obese, and 26% were morbidly obese. The proportion of patients receiving LH increased as BMI category increased (Table 1). Among those with LH between 6% and 15% had an ER visit or rehospitalization in 30 days, and rates were higher among other hysterectomy modalities. Conclusions: This real-world analysis shows that LH is utilized in a high proportion of morbidly obese EC patients, despite that it is frequently deemed infeasible in this patient population. Although the rate of ER visits and rehospitalization is lower among LH patients than those undergoing traditional hysterectomy across all BMI strata, further research is needed to determine the optimal patient population to receive LH.[Table: see text]

scholarly journals Use of the Electronic Health Record to Track Continuity of Care in Neurological Surgery Residency

2014 ◽  
Vol 6 (3) ◽  
pp. 507-511 ◽  
Author(s):  
N. Scott Litofsky ◽  
Ali Farooqui ◽  
Tomoko Tanaka ◽  
Thor Norregaard
Keyword(s):  
Electronic Health Record ◽  
Continuity Of Care ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Surgery Patient ◽  
Progress Notes ◽  
Electronic Health ◽  
Operative Notes ◽  
Neurological Surgery

Abstract Background Continuity of care in neurological surgery includes preoperative planning, technical and cognitive operative experience, and postoperative follow-up. Determining the extent of continuity of care with duty hour limits is problematic. Objective We used electronic health record data to track continuity of care in a neurological surgery program and to assess changes in rotation requirements. Methods The electronic health record was surveyed for all dictated resident–neurological surgery patient encounters (excluding progress notes), discharge summaries, and bedside procedures (July 2009–November 2011). Encounters were designated as preoperative, operative, or postoperative and were grouped by postgraduate year (PGY)–1 through PGY-6. Results A total of 6382 dictations were reviewed, with 5231 (82.0%) pertinent to neurological surgery. Of the 1469 operative notes, 303 (20.6%) had a record of an encounter with the operating resident in either a postoperative or preoperative setting. Preoperative encounters totaled 10.1% (148 of 1469); postoperative, 5.1% (75 of 1469); and encounters with both were 5.4% (80 of 1469). Continuity of care was as follows: PGY-1, 13.8% (4 of 29); PGY-2, 17.4% (26 of 149); PGY-3, 29.0% (36 of 124); PGY-4, 24.8% (73 of 294); PGY-5, 28.8% (109 of 379); and PGY-6, 11.1% (55 of 494). One of the highest continuity rates was observed in a rotation specifically constructed to enhance continuity of care. Conclusions The electronic health record can be used to track resident continuity of care in neurological surgery. The primary operating resident saw the patient in nonoperative settings, such as general admission, clinic visitation, or consultation in 20.6% (303 of 1469) of cases.

scholarly journals Desiderata for the development of next-generation electronic health record phenotype libraries

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Martin Chapman ◽  
Shahzad Mumtaz ◽  
Luke V Rasmussen ◽  
Andreas Karwath ◽  
Georgios V Gkoutos ◽  
...  
Keyword(s):  
Best Practices ◽  
Electronic Health Record ◽  
Health Record ◽  
Next Generation ◽  
Electronic Health Record Data ◽  
High Quality ◽  
Record Data ◽  
Electronic Health ◽  
Further Development

Abstract Background High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. Methods A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.

scholarly journals Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study (Preprint)

2018 ◽  
Author(s):  
Azraa Amroze ◽  
Terry S Field ◽  
Hassan Fouayzi ◽  
Devi Sundaresan ◽  
Laura Burns ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Primary Care Physicians ◽  
Controlled Trial ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Descriptive Study ◽  
Health Record ◽  
Log Data ◽  
Electronic Health

BACKGROUND Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study.

scholarly journals Electronic Health Record–Based Strategy to Promote Medication Adherence Among Patients With Diabetes: Longitudinal Observational Study

10.2196/13499 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e13499 ◽  
Author(s):  
Stacy Cooper Bailey ◽  
Amisha Wallia ◽  
Sarah Wright ◽  
Guisselle A Wismer ◽  
Alexandra C Infanzon ◽  
...  
Keyword(s):  
Medication Adherence ◽  
Electronic Health Record ◽  
Low Cost ◽  
Clinical Decision ◽  
Health Record ◽  
Low Literacy ◽  
Minimal Impact ◽  
Patients With Diabetes ◽  
Electronic Health

Background Poor medication adherence is common; however, few mechanisms exist in clinical practice to monitor how patients take medications in outpatient settings. Objective This study aimed to pilot test the Electronic Medication Complete Communication (EMC2) strategy, a low-cost, sustainable approach that uses functionalities within the electronic health record to promote outpatient medication adherence and safety. Methods The EMC2 strategy was implemented in 2 academic practices for 14 higher-risk diabetes medications. The strategy included: (1) clinical decision support alerts to prompt provider counseling on medication risks, (2) low-literacy medication summaries for patients, (3) a portal-based questionnaire to monitor outpatient medication use, and (4) clinical outreach for identified concerns. We recruited adult patients with diabetes who were prescribed a higher-risk diabetes medication. Participants completed baseline and 2-week interviews to assess receipt of, and satisfaction with, intervention components. Results A total of 100 patients were enrolled; 90 completed the 2-week interview. Patients were racially diverse, 30.0% (30/100) had a high school education or less, and 40.0% (40/100) had limited literacy skills. About a quarter (28/100) did not have a portal account; socioeconomic disparities were noted in account ownership by income and education. Among patients with a portal account, 58% (42/72) completed the questionnaire; 21 of the 42 patients reported concerns warranting clinical follow-up. Of these, 17 were contacted by the clinic or had their issue resolved within 24 hours. Most patients (33/38, 89%) who completed the portal questionnaire and follow-up interview reported high levels of satisfaction (score of 8 or greater on a scale of 1-10). Conclusions Findings suggest that the EMC2 strategy can be reliably implemented and delivered to patients, with high levels of satisfaction. Disparities in portal use may restrict intervention reach. Although the EMC2 strategy can be implemented with minimal impact on clinic workflow, future trials are needed to evaluate its effectiveness to promote adherence and safety.

scholarly journals Experiences of a National Web-Based Heart Age Calculator for Cardiovascular Disease Prevention: User Characteristics, Heart Age Results, and Behavior Change Survey (Preprint)

2020 ◽  
Author(s):  
Carissa Bonner ◽  
Natalie Raffoul ◽  
Tanya Battaglia ◽  
Julie Anne Mitchell ◽  
Carys Batcup ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Experimental Studies ◽  
Emotional Responses ◽  
Lifestyle Changes ◽  
Small Samples ◽  
Cvd Risk ◽  
Web Based ◽  
Heart Age

BACKGROUND Heart age calculators are used worldwide to engage the public in cardiovascular disease (CVD) prevention. Experimental studies with small samples have found mixed effects of these tools, and previous reports of population samples that used web-based heart age tools have not evaluated psychological and behavioral outcomes. OBJECTIVE This study aims to report on national users of the Australian heart age calculator and the follow-up of a sample of users. METHODS The heart age calculator was launched in 2019 by the National Heart Foundation of Australia. Heart age results were calculated for all users and recorded for those who signed up for a heart age report and an email follow-up over 10 weeks, after which a survey was conducted. CVD risk factors, heart age results, and psychological and behavioral questions were analyzed using descriptive statistics and chi-square tests. Open responses were thematically coded. RESULTS There were 361,044 anonymous users over 5 months, of which 30,279 signed up to receive a heart age report and 1303 completed the survey. There were more women (19,840/30,279, 65.52%), with an average age of 55.67 (SD 11.43) years, and most users knew blood pressure levels (20,279/30,279, 66.97%) but not cholesterol levels (12,267/30,279, 40.51%). The average heart age result was 4.61 (SD 4.71) years older than the current age, including (23,840/30,279, 78.73%) with an older heart age. For the survey, most users recalled their heart age category (892/1303, 68.46%), and many reported lifestyle improvements (diet 821/1303, 63.01% and physical activity 809/1303, 62.09%). People with an older heart age result were more likely to report a doctor visit (538/1055, 51.00%). Participants indicated strong emotional responses to heart age, both positive and negative. CONCLUSIONS Most Australian users received an older heart age as per international and UK heart age tools. Heart age reports with follow-up over 10 weeks prompted strong emotional responses, high recall rates, and self-reported lifestyle changes and clinical checks for more than half of the survey respondents. These findings are based on a more engaged user sample than previous research, who were more likely to know blood pressure and cholesterol values. Further research is needed to determine which aspects are most effective in initiating and maintaining lifestyle changes. The results confirm high public interest in heart age tools, but additional support is needed to help users understand the results and take appropriate action.

scholarly journals The PCORnet Blood Pressure Control Laboratory

2020 ◽  
Vol 13 (3) ◽  
Author(s):  
Mark J. Pletcher ◽  
Valy Fontil ◽  
Thomas Carton ◽  
Kathryn M. Shaw ◽  
Myra Smith ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Electronic Health Record ◽  
Blood Pressure Control ◽  
Comparative Effectiveness ◽  
Pressure Control ◽  
Health Record ◽  
Control Laboratory ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Background: Uncontrolled blood pressure (BP) is a leading preventable cause of death that remains common in the US population despite the availability of effective medications. New technology and program innovation has high potential to improve BP but may be expensive and burdensome for patients, clinicians, health systems, and payers and may not produce desired results or reduce existing disparities in BP control. Methods and Results: The PCORnet Blood Pressure Control Laboratory is a platform designed to enable national surveillance and facilitate quality improvement and comparative effectiveness research. The platform uses PCORnet, the National Patient-Centered Clinical Research Network, for engagement of health systems and collection of electronic health record data, and the Eureka Research Platform for eConsent and collection of patient-reported outcomes and mHealth data from wearable devices and smartphones. Three demonstration projects are underway: BP track will conduct national surveillance of BP control and related clinical processes by measuring theory-derived pragmatic BP control metrics using electronic health record data, with a focus on tracking disparities over time; BP MAP will conduct a cluster-randomized trial comparing effectiveness of 2 versions of a BP control quality improvement program; BP Home will conduct an individual patient-level randomized trial comparing effectiveness of smartphone-linked versus standard home BP monitoring. Thus far, BP Track has collected electronic health record data from over 826 000 eligible patients with hypertension who completed ≈3.1 million ambulatory visits. Preliminary results demonstrate substantial room for improvement in BP control (<140/90 mm Hg), which was 58% overall, and in the clinical processes relevant for BP control. For example, only 12% of patients with hypertension with a high BP measurement during an ambulatory visit received an order for a new antihypertensive medication. Conclusions: The PCORnet Blood Pressure Control Laboratory is designed to be a reusable platform for efficient surveillance and comparative effectiveness research; results from demonstration projects are forthcoming.

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